Autonomous Flight Using UWB-Based Positioning System with Optical Flow Sensors in a GPS-Denied Environment

This study presents the positioning method and autonomous flight of a quadrotor drone using ultra-wideband (UWB) communication and an optical flow sensor. UWB communication obtains the distance between multiple ground stations and a mobile station on a robot, and the position is calculated based on a multilateration method similar to global positioning system (GPS). The update rate of positioning using only UWB communication devices is slow;hence, we improved the update rate by combining the UWB and inertial measurement unit (IMU) sensor in the prior study. This study demonstrates the improvement of the positioning method and accuracy by sensor fusion of the UWB device, an IMU, and an optical flow sensor using the extended Kalman filter. The proposed method is validated by hovering and position control experiments and also realizes a sufficient rate and accuracy for autonomous flight.

Development of Serious Games for the Rehabilitation of the Human Vertebral Spine for Home Care

With the occurrence of pandemics, such as COVID-19, which lead to social isolation, there is a need for home rehabilitation procedures without the direct supervision of health professionals. The great difficulty of treatment at home is the cost of the conventional equipment and the need for specialized labor to operate it. Thus, this paper aimed to develop serious games to assist health professionals in the physiotherapy of patients with spinal pain for clinical and home applications. Serious games integrate serious aspects such as teaching, rehabilitation, and information with the playful and interactive elements of video games. Despite the positive indication and benefits of physiotherapy for cases of chronic spinal pain, the long treatment time, social isolation due to pandemics, and lack of motivation to use traditional methods are some of the main causes of therapeutic failure. Using Unity 3D (version 2019.4.24f1) software and a personal computer with a webcam, we developed aesthetically pleasing, smooth, and attractive games, while maintaining the essence of seriousness that is required for rehabilitation. The serious games, controlled using OpenPose (version v1.0.0alpha-1.5.0) software, were tested with a healthy volunteer. The findings demonstrated that the proposed games can be used as a playful tool to motivate patients during physiotherapy and to reduce cases of treatment abandonment, including at home.

RAS New Quadruped Robot Challenges: Pushing the Boundaries of Robotics Research and Development [Competitions]

Competitions have been a regular feature at almost every ICRA and IROS conference. So far, the organization of competitions has been entirely up to the conference organizing committee (OC). The OC usually issues a call for competition proposals;collects them;and decides which competitions will be hosted based on the conference site and budget conditions. Many big companies, such as Amazon, Airbus, and DJI, have successfully organized competitions at ICRA and IROS, some of which have continued for multiple years and have driven technological advances in robotics. The OC may also organize competitions based on the interests of its financial supporters. Many such competitions have been one-time events, but the Autonomous Drone Racing competition, which ran for four years until it was interrupted by the COVID-19 pandemic, was a successful testbed for vision-based autonomous navigation of quadcopters and attracted participation from many top-tier research teams.

Robotics Innovation for the Elderly [From the Editor's Desk]

As I write this editorial, it is the beginning of 2023. I would like to wish you and your loved ones a happy and healthy new year. We hope that 2023 will be a better year. Year 2022 was difficult for those in the war zone and those who struggled with policies on the COVID-19 pandemic. While the pandemic is at the back of most people's minds in many countries, as China abruptly abandoned its zero-COVID policy, hospitals in China became overcrowded very quickly, and the health-care system has faced a major stress test since December 2022. Sadly, among the most vulnerable populations, older adults were hit badly and could not get needed help in time, which led to many deaths. We need to take care of the elderly and work on robotics innovations to assist them.

VLP Landmark and SLAM-Assisted Automatic Map Calibration for Robot Navigation with Semantic Information

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.

Application of Deep Learning in the Deployment of an Industrial SCARA Machine for Real-Time Object Detection

In the spirit of innovation, the development of an intelligent robot system incorporating the basic principles of Industry 4.0 was one of the objectives of this study. With this aim, an experimental application of an industrial robot unit in its own isolated environment was carried out using neural networks. In this paper, we describe one possible application of deep learning in an Industry 4.0 environment for robotic units. The image datasets required for learning were generated using data synthesis. There are significant benefits to the incorporation of this technology, as old machines can be smartened and made more efficient without additional costs. As an area of application, we present the preparation of a robot unit which at the time it was originally produced and commissioned was not capable of using machine learning technology for object-detection purposes. The results for different scenarios are presented and an overview of similar research topics on neural networks is provided. A method for synthetizing datasets of any size is described in detail. Specifically, the working domain of a given robot unit, a possible solution to compatibility issues and the learning of neural networks from 3D CAD models with rendered images will be discussed.

Local Discrimination Based on Piezoelectric Sensing in Robots Composed of Soft Matter with Different Physical Properties

The coronavirus epidemic has attracted significant attention to the applications of pet robots which can be used to treat and entertain people in their homes. However, pet robots are fabricated using hard materials and it is difficult for them to communicate with people through contact. Soft robots are expected to realize communication through contact similar to that of actual pets. Soft robots provide people with a sense of healing and security owing to their softness and can extract rich information through external stimuli by applying a machine learning framework called physical-reservoir computing. It is crucial to determine the differences between the physical properties of soft materials that affect the information extracted from a soft body to develop an intelligent soft robot. In this study, two owl-shaped soft robots with different softnesses were developed to analyze the characteristics of the signal data obtained via piezoelectric film sensors embedded in models with different physical properties. An accuracy of 94.2% and 95.9% was obtained for touched part classification using 1D CNN and logistic regression models, respectively. Additionally, the relationship between the softness of material and classification performance was investigated by comparing the distribution of part classification accuracy for different hyper-parameters of two owl models.

Optimizing Cycle Time of Industrial Robotic Tasks with Multiple Feasible Configurations at the Working Points

Industrial robot applications should be designed to allow the robot to provide the best performance for increasing throughput. In this regard, both trajectory and task order optimization are crucial, since they can heavily impact cycle time. Moreover, it is very common for a robotic application to be kinematically or functionally redundant so that multiple arm configurations may fulfill the same task at the working points. In this context, even if the working cycle is composed of a small number of points, the number of possible sequences can be very high, so that the robot programmer usually cannot evaluate them all to obtain the shortest possible cycle time. One of the most well-known problems used to define the optimal task order is the Travelling Salesman Problem (TSP), but in its original formulation, it does not allow to consider different robot configurations at the same working point. This paper aims at overcoming TSP limitations by adding some mathematical and conceptual constraints to the problem. With such improvements, TSP can be used successfully to optimize the cycle time of industrial robotic tasks where multiple configurations are allowed at the working points. Simulation and experimental results are presented to assess how cost (cycle time) and computational time are influenced by the proposed implementation.

Multi-Domain Dynamic Modelling of a Low-Cost Upper Limb Rehabilitation Robot

Tracking patient progress through a course of robotic tele-rehabilitation requires constant position data logging and comparison, alongside periodic testing with no powered assistance. The test data must be compared with previous test attempts and an ideal baseline, for which a good understanding of the dynamics of the robot is required. The traditional dynamic modelling techniques for serial chain robotics, which involve forming and solving equations of motion, do not adequately describe the multi-domain phenomena that affect the movement of the rehabilitation robot. In this study, a multi-domain dynamic model for an upper limb rehabilitation robot is described. The model, built using a combination of MATLAB, SimScape, and SimScape Multibody, comprises the mechanical electro-mechanical and control domains. The performance of the model was validated against the performance of the robot when unloaded and when loaded with a human arm proxy. It is shown that this combination of software is appropriate for building a dynamic model of the robot and provides advantages over the traditional modelling approach. It is demonstrated that the responses of the model match the responses of the robot with acceptable accuracy, though the inability to model backlash was a limitation.